Merge pull request #3912 from pearjelly/master

修复zhipu-api向量化失败问题

server/knowledge_base/kb_cache/faiss_cache.py

@@ -57,7 +57,7 @@ class _FaissPool(CachePool):
     ) -> FAISS:
         embeddings = EmbeddingsFunAdapter(embed_model)
         doc = Document(page_content="init", metadata={})
-        vector_store = FAISS.from_documents([doc], embeddings, normalize_L2=True,distance_strategy="METRIC_INNER_PRODUCT")
+        vector_store = FAISS.from_documents([doc], embeddings, distance_strategy="METRIC_INNER_PRODUCT")
         ids = list(vector_store.docstore._dict.keys())
         vector_store.delete(ids)
         return vector_store
@@ -94,7 +94,7 @@ class KBFaissPool(_FaissPool):
 
                 if os.path.isfile(os.path.join(vs_path, "index.faiss")):
                     embeddings = self.load_kb_embeddings(kb_name=kb_name, embed_device=embed_device, default_embed_model=embed_model)
-                    vector_store = FAISS.load_local(vs_path, embeddings, normalize_L2=True,distance_strategy="METRIC_INNER_PRODUCT")
+                    vector_store = FAISS.load_local(vs_path, embeddings, distance_strategy="METRIC_INNER_PRODUCT")
                 elif create:
                     # create an empty vector store
                     if not os.path.exists(vs_path):

server/model_workers/zhipu.py

@@ -1,6 +1,7 @@
 from contextlib import contextmanager
 
 import httpx
+import requests
 from fastchat.conversation import Conversation
 from httpx_sse import EventSource
 
@@ -44,7 +45,7 @@ class ChatGLMWorker(ApiModelWorker):
     def __init__(
             self,
             *,
-            model_names: List[str] = ["zhipu-api"],
+            model_names: List[str] = ("zhipu-api",),
             controller_addr: str = None,
             worker_addr: str = None,
             version: Literal["glm-4"] = "glm-4",
@@ -87,27 +88,44 @@ class ChatGLMWorker(ApiModelWorker):
 
 
     def do_embeddings(self, params: ApiEmbeddingsParams) -> Dict:
+        embed_model = params.embed_model or self.DEFAULT_EMBED_MODEL
+
         params.load_config(self.model_names[0])
+        i = 0
+        batch_size = 1
+        result = []
+        while i < len(params.texts):
             token = generate_token(params.api_key, 60)
             headers = {
                 "Content-Type": "application/json",
                 "Authorization": f"Bearer {token}"
             }
-        i = 0
-        batch_size = 1
-        result = []
-        while i < len(params.texts):
             data = {
-                "model": params.embed_model or self.DEFAULT_EMBED_MODEL,
+                "model": embed_model,
                 "input": "".join(params.texts[i: i + batch_size])
             }
+            embedding_data = self.request_embedding_api(headers, data, 1)
+            if embedding_data:
+                result.append(embedding_data)
+            i += batch_size
+            print(f"请求{embed_model}接口处理第{i}块文本，返回embeddings: \n{embedding_data}")
+
+        return {"code": 200, "data": result}
+
+    # 请求接口，支持重试
+    def request_embedding_api(self, headers, data, retry=0):
+        response = ''
+        try:
             url = "https://open.bigmodel.cn/api/paas/v4/embeddings"
             response = requests.post(url, headers=headers, json=data)
             ans = response.json()
-            result.append(ans["data"][0]["embedding"])
-            i += batch_size
-
-        return {"code": 200, "data": result}
+            return ans["data"][0]["embedding"]
+        except Exception as e:
+            print(f"request_embedding_api error={e} \nresponse={response}")
+            if retry > 0:
+                return self.request_embedding_api(headers, data, retry - 1)
+            else:
+                return None
 
     def get_embeddings(self, params):
         print("embedding")